
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
warnings.filterwarnings('ignore')


df= pd.read_csv('audi.csv')
df.head(5)


df.shape

(10668, 9)


len(df)

10668


df.size

96012


df.isnull().sum()

model           0
year            0
price           0
transmission    0
mileage         0
fuelType        0
tax             0
mpg             0
engineSize      0
dtype: int64


df.dtypes

model            object
year              int64
price             int64
transmission     object
mileage           int64
fuelType         object
tax               int64
mpg             float64
engineSize      float64
dtype: object


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10668 entries, 0 to 10667
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   model         10668 non-null  object 
 1   year          10668 non-null  int64  
 2   price         10668 non-null  int64  
 3   transmission  10668 non-null  object 
 4   mileage       10668 non-null  int64  
 5   fuelType      10668 non-null  object 
 6   tax           10668 non-null  int64  
 7   mpg           10668 non-null  float64
 8   engineSize    10668 non-null  float64
dtypes: float64(2), int64(4), object(3)
memory usage: 750.2+ KB


df.duplicated().sum()

103


df.loc[df.duplicated()]


df.describe()


df.describe(include='object')


from ydata_profiling import ProfileReport


ProfileReport(df, title='Profiling Report')

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]


df['model'].unique()

array([' A1', ' A6', ' A4', ' A3', ' Q3', ' Q5', ' A5', ' S4', ' Q2',
       ' A7', ' TT', ' Q7', ' RS6', ' RS3', ' A8', ' Q8', ' RS4', ' RS5',
       ' R8', ' SQ5', ' S8', ' SQ7', ' S3', ' S5', ' A2', ' RS7'],
      dtype=object)


pd.DataFrame(df['model'].value_counts()).reset_index()


df['transmission'].unique()

array(['Manual', 'Automatic', 'Semi-Auto'], dtype=object)


df['transmission'].value_counts().to_frame()


df['fuelType'].value_counts().to_frame()


engine = df['engineSize'].value_counts().to_frame().reset_index()
engine.rename(columns={'index':'Engine_size','engineSize':'Counts'})


df.corr('pearson')


df.corr('spearman')


df.head()


df['year'].unique()

array([2017, 2016, 2019, 2015, 2014, 2018, 2013, 2020, 2004, 2009, 2012,
       2010, 2007, 2011, 2008, 2003, 2005, 2002, 2006, 1998, 1997],
      dtype=int64)


sns.scatterplot(data=df, x='year',y='price')
plt.show()


plt.figure(figsize=(12,4))
sns.heatmap(df.corr(),annot=True)
plt.show()


plt.figure(figsize=(12,4))
sns.barplot(data=df, x='year', y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.show()


plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
sns.lineplot(data=df, x='year',y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.subplot(1,3,2)
sns.barplot(data=df, x='model',y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.subplot(1,3,3)
sns.barplot(data=df, x='transmission',y='price')
ax=plt.gca()
ax.set_facecolor('black')
plt.show()


plt.figure(figsize=(12,5))
sns.barplot(data=df, x='fuelType', y='price',hue='transmission')
ax=plt.gca()
ax.set_facecolor('black')
plt.show()


sns.boxplot(data=df, x='transmission',y='price')
plt.show()


plt.figure(figsize=(12,4))
sns.boxplot(df['price'])
plt.show()


display(df[['transmission','price']].describe())


q1 = df['price'].quantile(0.25)
q3 = df['price'].quantile(0.75)


q1,q3

(15130.75, 27990.0)


IQR = q3-q1
IQR

12859.25


upper_whisker = q3+1.5*IQR


upper_whisker

47278.875


df[df['price'] > 47278.875]


lower_whisker = q1-1.5*IQR
lower_whisker

-4158.125


df[df['price'] < -4158.125]


df.groupby('transmission')['price'].count()

transmission
Automatic    2708
Manual       4369
Semi-Auto    3591
Name: price, dtype: int64


mannual = df[df['transmission']=='Manual']['price'].to_frame()
mannual


mannual.describe()


m1 = mannual['price'].quantile(0.25)
m3 = mannual['price'].quantile(0.75)


MIQR = m3-m1


mupper_whisker = m3+1.5*MIQR
mupper_whisker

30320.0


mannual[mannual['price'] > 30320.0].T


automatic = df[df['transmission']=='Automatic']['price'].to_frame()
automatic.T


a1 = automatic['price'].quantile(0.25)
a3 = automatic['price'].quantile(0.75)


AIQR = a3-a1


aupper_whisker = a3+1.5*AIQR
aupper_whisker

54801.5


automatic[automatic['price'] > 54801.5].T


semi_auto = df[df['transmission']=='Semi-Auto']['price'].to_frame()
semi_auto.T


sa1 = semi_auto['price'].quantile(0.25)
sa3 = semi_auto['price'].quantile(0.75)


SAIQR = sa3-sa1


saupper_whisker = sa3+1.5*SAIQR
saupper_whisker

50268.0


automatic[automatic['price'] > 50268.0].T


sns.pairplot(df)
plt.show()


df.sample(10)


x = df.iloc[:,[0,1,3,4,5,6,7,8]].values
x

array([[' A1', 2017, 'Manual', ..., 150, 55.4, 1.4],
       [' A6', 2016, 'Automatic', ..., 20, 64.2, 2.0],
       [' A1', 2016, 'Manual', ..., 30, 55.4, 1.4],
       ...,
       [' A3', 2020, 'Manual', ..., 150, 49.6, 1.0],
       [' Q3', 2017, 'Automatic', ..., 150, 47.9, 1.4],
       [' Q3', 2016, 'Manual', ..., 150, 47.9, 1.4]], dtype=object)


x.shape

(10668, 8)


y = df.iloc[:,[2]].values
y

array([[12500],
       [16500],
       [11000],
       ...,
       [17199],
       [19499],
       [15999]], dtype=int64)


y.shape

(10668, 1)


pd.DataFrame(x).head(5)


pd.DataFrame(y).head(5)


from sklearn.preprocessing import LabelEncoder


le1 = LabelEncoder()
le1

LabelEncoder()

LabelEncoder()


x[:,0]

array([' A1', ' A6', ' A1', ..., ' A3', ' Q3', ' Q3'], dtype=object)


x[:,0] = le1.fit_transform(x[:,0])
x[:,0]

array([0, 5, 0, ..., 2, 9, 9], dtype=object)


le2 = LabelEncoder()
le2

LabelEncoder()

LabelEncoder()


x[:,4]

array(['Petrol', 'Diesel', 'Petrol', ..., 'Petrol', 'Petrol', 'Petrol'],
      dtype=object)


x[:,4]=le2.fit_transform(x[:,4])
x[:,4]

array([2, 0, 2, ..., 2, 2, 2], dtype=object)


pd.DataFrame(x).head(5)


from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer


ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[2])],remainder='passthrough')
ct

ColumnTransformer(remainder='passthrough',
                  transformers=[('encoder', OneHotEncoder(), [2])])

ColumnTransformer(remainder='passthrough',
                  transformers=[('encoder', OneHotEncoder(), [2])])

[2]

OneHotEncoder()

passthrough


x = ct.fit_transform(x)
x

array([[0.0, 1.0, 0.0, ..., 150, 55.4, 1.4],
       [1.0, 0.0, 0.0, ..., 20, 64.2, 2.0],
       [0.0, 1.0, 0.0, ..., 30, 55.4, 1.4],
       ...,
       [0.0, 1.0, 0.0, ..., 150, 49.6, 1.0],
       [1.0, 0.0, 0.0, ..., 150, 47.9, 1.4],
       [0.0, 1.0, 0.0, ..., 150, 47.9, 1.4]], dtype=object)


x.shape

(10668, 10)


pd.DataFrame(x)


from sklearn.preprocessing import StandardScaler


sc = StandardScaler()
sc

StandardScaler()

StandardScaler()


x = sc.fit_transform(x)
x

array([[-0.58326752,  1.2007284 , -0.71233307, ...,  0.35714729,
         0.35755001, -0.88021837],
       [ 1.71447913, -0.83282781, -0.71233307, ..., -1.57832278,
         1.03713001,  0.11492465],
       [-0.58326752,  1.2007284 , -0.71233307, ..., -1.42944047,
         0.35755001, -0.88021837],
       ...,
       [-0.58326752,  1.2007284 , -0.71233307, ...,  0.35714729,
        -0.09035499, -1.54364705],
       [ 1.71447913, -0.83282781, -0.71233307, ...,  0.35714729,
        -0.22163749, -0.88021837],
       [-0.58326752,  1.2007284 , -0.71233307, ...,  0.35714729,
        -0.22163749, -0.88021837]])


pd.DataFrame(x)


from sklearn.model_selection import train_test_split


 X_train, X_test, y_train, y_test = train_test_split(x,y, random_state=0, test_size=0.2)


x.shape

(10668, 10)


y.shape

(10668, 1)


X_train.shape, y_train.shape

((8534, 10), (8534, 1))


X_test.shape, y_test.shape

((2134, 10), (2134, 1))


from sklearn.ensemble import RandomForestRegressor


reg = RandomForestRegressor(random_state=0)
reg

RandomForestRegressor(random_state=0)

RandomForestRegressor(random_state=0)


reg.fit(X_train, y_train)

RandomForestRegressor(random_state=0)

RandomForestRegressor(random_state=0)


display(reg)

RandomForestRegressor(random_state=0)

RandomForestRegressor(random_state=0)


y_pred = reg.predict(X_test)
y_pred

array([14337.15, 23450.35, 27330.07, ..., 46275.18, 31359.  ,  9929.62])


len(y_pred)

2134


len(y_test)

2134


y_pred.reshape(len(y_pred),1)

array([[14337.15],
       [23450.35],
       [27330.07],
       ...,
       [46275.18],
       [31359.  ],
       [ 9929.62]])


y_test.reshape(len(y_test),1)

array([[14998],
       [21950],
       [28990],
       ...,
       [45995],
       [30500],
       [ 8400]], dtype=int64)


np.concatenate((y_pred.reshape(len(y_pred),1),y_test.reshape(len(y_test),1)),1)

array([[14337.15, 14998.  ],
       [23450.35, 21950.  ],
       [27330.07, 28990.  ],
       ...,
       [46275.18, 45995.  ],
       [31359.  , 30500.  ],
       [ 9929.62,  8400.  ]])


from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error


print("The Accuracy for the model as r2_score will have :",r2_score(y_test,y_pred))
print("+"*70)

The Accuracy for the model as r2_score will have : 0.9536134841307546
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


print("The mean absolute error for the model we have",mean_absolute_error(y_test,y_pred))
print("+"*70)

The mean absolute error for the model we have 1538.730980670462
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


from sklearn.linear_model import LinearRegression


ler = LinearRegression()
ler

LinearRegression()

LinearRegression()


ler.fit(X_train, y_train)

LinearRegression()

LinearRegression()

ler

LinearRegression()

LinearRegression()


y_predler = ler.predict(X_test)
y_predler

array([[13053.11094043],
       [29339.11094043],
       [31899.11094043],
       ...,
       [42661.11094043],
       [31567.11094043],
       [ 7286.11094043]])


y_test

array([[14998],
       [21950],
       [28990],
       ...,
       [45995],
       [30500],
       [ 8400]], dtype=int64)


np.concatenate((y_predler.reshape(len(y_predler),1), y_test.reshape(len(y_test),1)),1)

array([[13053.11094043, 14998.        ],
       [29339.11094043, 21950.        ],
       [31899.11094043, 28990.        ],
       ...,
       [42661.11094043, 45995.        ],
       [31567.11094043, 30500.        ],
       [ 7286.11094043,  8400.        ]])


from sklearn.metrics import r2_score, mean_absolute_error


print("The Accuracy for model Linear will be :",r2_score(y_test, y_predler))
print("+"*55)

The Accuracy for model Linear will be : 0.79159249215952
+++++++++++++++++++++++++++++++++++++++++++++++++++++++


print("The mean sqaure error for the Linear model will be: ",mean_absolute_error(y_test, y_predler))
print("+"*70)

The mean sqaure error for the Linear model will be:  3381.661027107596
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


y_predlecom = ler.predict(x)
y_predlecom

array([[14619.11094043],
       [20627.11094043],
       [13806.11094043],
       ...,
       [19393.11094043],
       [20990.11094043],
       [16681.11094043]])


result = pd.concat([df,pd.DataFrame(y_predlecom)],axis=1)
result = result.rename(columns={0:"Predicted_Price"})
result['Difference'] = result['Predicted_Price'] - result['price']
result.sample(10)


from sklearn.ensemble import ExtraTreesRegressor


ET_Model = ExtraTreesRegressor(n_estimators=120)
ET_Model

ExtraTreesRegressor(n_estimators=120)

ExtraTreesRegressor(n_estimators=120)


ET_Model.fit(X_train, y_train)

ExtraTreesRegressor(n_estimators=120)

ExtraTreesRegressor(n_estimators=120)


y_predetr =  ET_Model.predict(X_test)
y_predetr

array([14017.575     , 22288.375     , 28787.33333333, ...,
       49635.35833333, 30444.25      ,  9988.24166667])


y_test

array([[14998],
       [21950],
       [28990],
       ...,
       [45995],
       [30500],
       [ 8400]], dtype=int64)


print("The Accuracy for the Extra Tree Regressor is :",r2_score(y_test, y_predetr))
print("+"*65)

The Accuracy for the Extra Tree Regressor is : 0.9566191355841895
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


print("The mean absolute error for the Extra Tree Regressor is :",mean_absolute_error(y_test, y_predetr))
print("+"*75)

The mean absolute error for the Extra Tree Regressor is : 1539.3485770071852
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++


y_predetcom = ET_Model.predict(x)
y_predetcom

array([12500.        , 16500.        , 11000.        , ...,
       17199.        , 19499.        , 18056.00833333])


resultet = pd.concat([df,pd.DataFrame(y_predetcom)],axis=1)
resultet = resultet.rename(columns={0:"Predicted_value"})
resultet['Difference'] = resultet['Predicted_value'] - resultet['price']
resultet.sample(20)


from sklearn.model_selection import RandomizedSearchCV


n_estimators = [int(x) for x in np.linspace(start = 80, stop = 1500, num = 10)]


n_estimators

[80, 237, 395, 553, 711, 868, 1026, 1184, 1342, 1500]


max_features = ['auto', 'sqrt']
max_features

['auto', 'sqrt']


max_depth = [int(x) for x in np.linspace(6, 45, num = 5)]
max_depth

[6, 15, 25, 35, 45]


min_samples_split = [2, 5, 10, 15, 100]
min_samples_split

[2, 5, 10, 15, 100]


min_samples_leaf = [1, 2, 5, 10]
min_samples_leaf

[1, 2, 5, 10]


rand_grid={'n_estimators': n_estimators,
               'max_features': max_features,
               'max_depth': max_depth,
               'min_samples_split': min_samples_split,
               'min_samples_leaf': min_samples_leaf}


rand_grid

{'n_estimators': [80, 237, 395, 553, 711, 868, 1026, 1184, 1342, 1500],
 'max_features': ['auto', 'sqrt'],
 'max_depth': [6, 15, 25, 35, 45],
 'min_samples_split': [2, 5, 10, 15, 100],
 'min_samples_leaf': [1, 2, 5, 10]}


rf = RandomForestRegressor()
rf

RandomForestRegressor()

RandomForestRegressor()


rCV=RandomizedSearchCV(estimator=rf,param_distributions=rand_grid,scoring='neg_mean_squared_error',n_iter=3,cv=3,random_state=42, n_jobs = 1)

rCV

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')

RandomForestRegressor()

RandomForestRegressor()


rCV.fit(X_train,y_train)

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')

RandomizedSearchCV(cv=3, estimator=RandomForestRegressor(), n_iter=3, n_jobs=1,
                   param_distributions={'max_depth': [6, 15, 25, 35, 45],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'n_estimators': [80, 237, 395, 553, 711,
                                                         868, 1026, 1184, 1342,
                                                         1500]},
                   random_state=42, scoring='neg_mean_squared_error')

RandomForestRegressor()

RandomForestRegressor()


rf_pred = rCV.predict(X_test)
rf_pred

array([14099.8108772 , 23761.77940499, 28500.80689889, ...,
       47952.547461  , 31290.62208548, 10063.56027917])


from sklearn.metrics import mean_absolute_error, mean_squared_error


print("Mean aboslute error for model :",mean_absolute_error(y_test,rf_pred))
print("+"*50)

Mean aboslute error for model : 1501.6334599901706
++++++++++++++++++++++++++++++++++++++++++++++++++


print("Mean Sqaured error for model :",mean_squared_error(y_test, rf_pred))
print("+"*50)

Mean Sqaured error for model : 5704930.425204219
++++++++++++++++++++++++++++++++++++++++++++++++++


print("The Accuracy for the Model will be :",r2_score(y_test, rf_pred))
print("+"*50)

The Accuracy for the Model will be : 0.958484238238705
++++++++++++++++++++++++++++++++++++++++++++++++++


from catboost import CatBoostRegressor


cat = CatBoostRegressor()
cat

<catboost.core.CatBoostRegressor at 0x2ae3d268fa0>


cat.fit(X_train, y_train)

Learning rate set to 0.057452
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<catboost.core.CatBoostRegressor at 0x2ae3d268fa0>


cat_pred = cat.predict(X_test)
cat_pred

array([13386.63817795, 24056.86842882, 28082.99514751, ...,
       45959.31234642, 31714.44751009,  9481.45994163])


print("The Accuracy for the Catboost model analysing are :",r2_score(y_test, cat_pred))
print("+"*70)

The Accuracy for the Catboost model analysing are : 0.9641612028134969
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

	year	price	mileage	tax	mpg	engineSize
count	10668.000000	10668.000000	10668.000000	10668.000000	10668.000000	10668.000000
mean	2017.100675	22896.685039	24827.244001	126.011436	50.770022	1.930709
std	2.167494	11714.841888	23505.257205	67.170294	12.949782	0.602957
min	1997.000000	1490.000000	1.000000	0.000000	18.900000	0.000000
25%	2016.000000	15130.750000	5968.750000	125.000000	40.900000	1.500000
50%	2017.000000	20200.000000	19000.000000	145.000000	49.600000	2.000000
75%	2019.000000	27990.000000	36464.500000	145.000000	58.900000	2.000000
max	2020.000000	145000.000000	323000.000000	580.000000	188.300000	6.300000

	year	price	mileage	tax	mpg	engineSize
year	1.000000	0.592581	-0.789667	0.093066	-0.351281	-0.031582
price	0.592581	1.000000	-0.535357	0.356157	-0.600334	0.591262
mileage	-0.789667	-0.535357	1.000000	-0.166547	0.395103	0.070710
tax	0.093066	0.356157	-0.166547	1.000000	-0.635909	0.393075
mpg	-0.351281	-0.600334	0.395103	-0.635909	1.000000	-0.365621
engineSize	-0.031582	0.591262	0.070710	0.393075	-0.365621	1.000000

	year	price	mileage	tax	mpg	engineSize
year	1.000000	0.765990	-0.862416	0.194361	-0.532725	0.026446
price	0.765990	1.000000	-0.709120	0.422473	-0.749557	0.465471
mileage	-0.862416	-0.709120	1.000000	-0.180303	0.530829	0.034433
tax	0.194361	0.422473	-0.180303	1.000000	-0.601924	0.303448
mpg	-0.532725	-0.749557	0.530829	-0.601924	1.000000	-0.368087
engineSize	0.026446	0.465471	0.034433	0.303448	-0.368087	1.000000

	price
count	10668.000000
mean	22896.685039
std	11714.841888
min	1490.000000
25%	15130.750000
50%	20200.000000
75%	27990.000000
max	145000.000000

	price
0	12500
2	11000
4	17300
7	11750
8	10200
...	...
10662	12695
10663	16999
10664	16999
10665	17199
10667	15999

CarPrice_Pridction Business_Case Project ML¶

Importing all Neccesary Libraries¶

Profiling Report¶

Basic standard correlation coefficient¶

Spearman rank correlation¶

Outlier Detection for Price¶

Finding the Outliers as Inter Quantile Range¶

Outliers for Data¶

Outliers Detection for Mannual Car Price¶

Outliers Detection for Automatic Car Price¶

Outliers Detection for Semi_Automatic Car Price¶

Label Encoding¶

Label Encoding for Model and fuel type Column¶

One hot Encoding for Transmission type¶

Standardization Featuring¶

Traning and Testing models¶

Analysing the model with Random Forest Regressor¶

Prediciting the test Data Values¶

Analyse the model with Accuracy and Mean Absolute Error¶

Analysing the model for Linear Regression¶

Predicting the model¶

Analaysing the Accuracy and Mean Absolute Error¶

Analysing the model with the Extra Tree Regressor¶

Analysing the Accuracy and mean sqauare error¶

Analysing the RandomizedSearchCV¶

Creating the random Grid¶

Predicting the model¶

Analysing the model with Mean_absolute_error and mean_squared_error¶

Accuracy for Model¶

Analysing with the Cat boost Regressor¶

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize
0	A1	2017	12500	Manual	15735	Petrol	150	55.4	1.4
1	A6	2016	16500	Automatic	36203	Diesel	20	64.2	2.0
2	A1	2016	11000	Manual	29946	Petrol	30	55.4	1.4
3	A4	2017	16800	Automatic	25952	Diesel	145	67.3	2.0
4	A3	2019	17300	Manual	1998	Petrol	145	49.6	1.0

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize
273	Q3	2019	34485	Automatic	10	Diesel	145	47.1	2.0
764	Q2	2019	22495	Manual	1000	Diesel	145	49.6	1.6
784	Q3	2015	13995	Manual	35446	Diesel	145	54.3	2.0
967	Q5	2019	31998	Semi-Auto	100	Petrol	145	33.2	2.0
990	Q2	2019	22495	Manual	1000	Diesel	145	49.6	1.6
...	...	...	...	...	...	...	...	...	...
9508	A4	2019	26990	Automatic	2250	Diesel	145	50.4	2.0
9521	Q3	2019	26990	Manual	10	Petrol	145	40.9	1.5
9529	Q5	2019	44990	Automatic	10	Diesel	145	36.2	2.0
9550	Q3	2019	29995	Manual	10	Petrol	145	39.8	1.5
9597	Q3	2019	28490	Manual	10	Diesel	145	42.8	2.0

	model	transmission	fuelType
count	10668	10668	10668
unique	26	3	3
top	A3	Manual	Diesel
freq	1929	4369	5577

	index	model
0	A3	1929
1	Q3	1417
2	A4	1381
3	A1	1347
4	A5	882
5	Q5	877
6	Q2	822
7	A6	748
8	Q7	397
9	TT	336
10	A7	122
11	A8	118
12	Q8	69
13	RS6	39
14	RS3	33
15	RS4	31
16	RS5	29
17	R8	28
18	S3	18
19	SQ5	16
20	S4	12
21	SQ7	8
22	S8	4
23	S5	3
24	A2	1
25	RS7	1

	Engine_size	Counts
0	2.0	5169
1	1.4	1594
2	3.0	1149
3	1.6	913
4	1.5	744
5	1.0	558
6	4.0	154
7	1.8	126
8	2.5	61
9	0.0	57
10	2.9	49
11	1.2	31
12	4.2	25
13	5.2	23
14	3.2	5
15	1.9	4
16	2.7	3
17	4.1	2
18	6.3	1

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize
198	Q7	2020	62985	Semi-Auto	10	Diesel	145	33.2	3.0
221	Q7	2019	49985	Automatic	10	Diesel	145	33.2	3.0
222	Q7	2019	59995	Automatic	10	Diesel	145	33.2	3.0
223	Q5	2020	47895	Semi-Auto	10	Petrol	145	30.7	2.0
247	Q7	2019	56985	Automatic	1510	Diesel	145	33.2	3.0
...	...	...	...	...	...	...	...	...	...
10530	RS6	2016	52950	Automatic	27000	Petrol	325	29.4	4.0
10546	A4	2020	49000	Automatic	38	Diesel	145	39.2	3.0
10597	Q7	2020	68000	Automatic	4162	Diesel	145	32.1	3.0
10602	Q7	2020	53792	Automatic	3182	Diesel	145	33.2	3.0
10648	RS6	2016	49990	Automatic	24000	Petrol	325	29.4	4.0

	price
count	4369.000000
mean	16101.033417
std	5519.089663
min	1699.000000
25%	11995.000000
50%	15700.000000
75%	19325.000000
max	47995.000000

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize
5208	A4	2018	21450	Semi-Auto	9555	Petrol	145	50.4	1.4
8535	A1	2016	8500	Manual	75869	Diesel	0	80.7	1.6
5935	A3	2019	23900	Manual	1529	Diesel	150	52.3	1.6
10559	Q2	2019	24500	Automatic	8334	Petrol	145	40.9	1.5
3551	A3	2013	11980	Semi-Auto	59386	Petrol	125	50.4	1.8
6730	A3	2017	23475	Semi-Auto	27524	Petrol	160	43.5	2.0
2926	A4	2018	23132	Semi-Auto	15475	Petrol	145	50.4	2.0
4888	A3	2017	13500	Manual	51578	Petrol	20	60.1	1.4
1800	A4	2017	16995	Automatic	21641	Petrol	145	51.4	1.4
2887	A3	2018	20971	Semi-Auto	22604	Petrol	145	56.5	1.5

	0	1	2	3	4	5	6	7	8	9
0	-0.583268	1.200728	-0.712333	-1.123544	-0.046450	-0.386836	1.050783	0.357147	0.357550	-0.880218
1	1.714479	-0.832828	-0.712333	-0.160831	-0.507834	0.483989	-0.954181	-1.578323	1.037130	0.114925
2	-0.583268	1.200728	-0.712333	-1.123544	-0.507834	0.217781	1.050783	-1.429440	0.357550	-0.880218
3	1.714479	-0.832828	-0.712333	-0.545916	-0.046450	0.047853	-0.954181	0.282706	1.276528	0.114925
4	-0.583268	1.200728	-0.712333	-0.738459	0.876318	-0.971285	1.050783	0.282706	-0.090355	-1.543647
...	...	...	...	...	...	...	...	...	...	...
10663	-0.583268	1.200728	-0.712333	-0.738459	1.337702	-0.885343	1.050783	0.282706	-0.090355	-1.543647
10664	-0.583268	1.200728	-0.712333	-0.738459	1.337702	-0.972136	1.050783	0.357147	-0.090355	-1.543647
10665	-0.583268	1.200728	-0.712333	-0.738459	1.337702	-1.030381	1.050783	0.357147	-0.090355	-1.543647
10666	1.714479	-0.832828	-0.712333	0.609339	-0.046450	-0.688442	1.050783	0.357147	-0.221637	-0.880218
10667	-0.583268	1.200728	-0.712333	0.609339	-0.507834	-0.551913	1.050783	0.357147	-0.221637	-0.880218

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize	Predicted_Price	Difference
4755	Q2	2016	19990	Semi-Auto	25000	Petrol	125	52.3	1.4	16485.11094	-3504.88906
3771	A3	2019	21000	Manual	4500	Diesel	145	51.4	1.6	22060.11094	1060.11094
4489	Q3	2019	34990	Automatic	7199	Petrol	145	32.1	2.0	34207.11094	-782.88906
8022	Q3	2017	17499	Automatic	34692	Petrol	145	47.9	1.4	18623.11094	1124.11094
6752	Q2	2020	25888	Semi-Auto	4000	Petrol	145	42.2	1.5	28861.11094	2973.11094
7387	Q2	2017	20490	Manual	10700	Petrol	150	50.4	1.4	18140.11094	-2349.88906
7240	A5	2019	25990	Automatic	10664	Diesel	145	50.4	2.0	28413.11094	2423.11094
9992	Q3	2020	34000	Automatic	1000	Petrol	145	31.4	2.0	36939.11094	2939.11094
8985	A4	2017	19599	Automatic	28367	Petrol	145	49.6	1.4	17269.11094	-2329.88906
1877	A5	2015	15750	Automatic	59886	Petrol	160	44.1	1.8	15302.11094	-447.88906

	model	year	price	transmission	mileage	fuelType	tax	mpg	engineSize	Predicted_value	Difference
998	Q5	2017	33495	Semi-Auto	37492	Petrol	150	34.0	3.0	33495.000000	0.000000
8022	Q3	2017	17499	Automatic	34692	Petrol	145	47.9	1.4	18031.516667	532.516667
4212	A1	2014	10398	Manual	24856	Petrol	125	52.3	1.4	10398.000000	0.000000
9035	A1	2015	12681	Manual	10747	Petrol	30	55.4	1.4	12681.000000	0.000000
1047	A6	2019	29995	Automatic	4985	Diesel	145	50.4	2.0	29995.000000	0.000000
691	A4	2017	17298	Manual	6402	Petrol	145	52.3	1.4	17298.000000	0.000000
7537	A4	2016	16500	Automatic	40882	Diesel	20	67.3	2.0	16045.166667	-454.833333
3485	Q5	2019	36991	Automatic	10	Diesel	145	38.2	2.0	37889.000000	898.000000
4501	A5	2019	31990	Manual	6000	Petrol	145	37.2	2.0	31990.000000	0.000000
2022	Q2	2019	21990	Semi-Auto	5445	Diesel	150	47.9	1.6	21990.000000	0.000000
353	A4	2020	40780	Semi-Auto	6746	Diesel	145	45.6	2.0	40780.000000	0.000000
1778	A4	2016	16750	Semi-Auto	50715	Diesel	30	65.7	2.0	16750.000000	0.000000
4834	A3	2019	21990	Manual	4875	Petrol	145	42.2	1.5	23310.233333	1320.233333
7274	A4	2016	16990	Manual	29588	Diesel	20	70.6	2.0	16990.000000	0.000000
1044	Q5	2017	32995	Semi-Auto	32821	Diesel	235	42.2	3.0	32995.000000	0.000000
6926	A8	2018	34399	Automatic	11300	Diesel	145	50.4	3.0	34399.000000	0.000000
1253	A3	2014	11995	Manual	24175	Petrol	20	60.1	1.4	11995.000000	0.000000
2521	A4	2018	18995	Manual	14912	Petrol	145	51.4	1.4	17869.333333	-1125.666667
221	Q7	2019	49985	Automatic	10	Diesel	145	33.2	3.0	59995.000000	10010.000000
6614	A4	2017	16000	Semi-Auto	26048	Diesel	145	72.4	2.0	16000.000000	0.000000